1. Field of the Invention
The present invention pertains to product demand forecasting. More particularly, this invention relates to a computer-implemented profile-based product demand forecasting system and a method thereof.
2. Description of the Related Art
As the world advances in technology, more and more new products are being introduced to replace older or technologically obsolete products. This tends to shorten product life-cycle for the products that are on the market. However, the shortened product life-cycle of a product adversely affects the profitability of the product. Thus, in order to maximize the profitability of a product with a shortened life-cycle, the manufacturer needs to reduce cost associated with producing and marketing the product. One way of reducing the cost is to minimize inventory of the product. This typically requires accurate forecast or prediction of future demand of the product. If the forecast is below the actual demand, the manufacturer may experience loss of sales. This may significantly affect the profitability of the product, particularly if the product is at the beginning of its life-cycle where profit margins are high. If the forecast exceeds the actual demand, the manufacturer may end up with a huge inventory of obsolescent and quickly depreciating product, particularly if the product is at the end of its life-cycle.
However, there is in general very little historical demand data available for predicating or forecasting the future demand of a product with a shortened product life-cycle. In addition, if a product has not been introduced, there will not be any historical demand data for the new product. Proposals have been made to use the demand data of similar products sold in the past to forecast the demand of the new product. There are, however, many problems associated with this prior proposal. One problem is that the demand data of similar products do not reflect the exact market condition under which the new product will be sold. Another problem is that even among the similar products, a newer product typically has a shorter product life-cycle than an older product. This causes different products to have different product life-cycle demand curves. In addition, the average sales rate for different products may be different from one another due to the change in technology and the rate at which a new product is being accepted by consumers. This typically produces different average sales rates.
Prior solutions have been proposed to solving these problems. One prior solution is referred to as time-series forecasting method. This method adds up all sales for a certain period in a certain market segment (e.g., high end servers). Then the method generates the demand curve based on monthly sales figures. The method can then (1) put a trend line on the demand curve, (2) generate a moving average to predict the trend, or (3) use known smoothing techniques to obtain the forecast.
However, this prior solution bears disadvantages. One disadvantage is that although the forecasts are relatively accurate on an aggregated level, the breakdown to the Stock-Keeping Unit (SKU) level is difficult and is a main source of error. SKU refers to the same end product unit boxed for sale. For example, the generic high end server computer may be available for sale at different memory and/or CPU speed configurations. In this case, there will different SKUs to track different configurations. The efficient use of this method depends on the stability of the ratios of demand of different products. In a short life-cycle situation, the ratios change rapidly.
Another prior proposal is referred to as time-series forecasting method on SKU. This method is similar to the above-described method, except that it looks at one particular SKU. However, this prior solution still bears disadvantages. One disadvantage is that there are a lot of noise from the actual randomness of demand, making the forecast unreliable. Another disadvantage is that it requires historic data for the forecasting. This also makes the prior method inapplicable for forecasting the demand of new products with short life-cycle because historic data are scarce in a short life-cycle environment.
Other prior solutions include the traditional manual approach in which human planners or forecasters predict the product demand using business judgment and “best guesses”. The manual forecasting is very labor intensive. The success of forecasting also depends crucially on the abilities and knowledge of the human forecasters. It typically does not provide accurate product demand forecast.